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Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems
We propose a trust-region stochastic sequential quadratic programming algorithm (TR-StoSQP) to solve nonlinear optimization problems …
Yuchen Fang
,
Sen Na
,
Michael W. Mahoney
,
Mladen Kolar
Cite
arXiv
An Asymptotically Optimal Method for Constrained Stochastic Optimization
We perform statistical inference for the solution of stochastic optimization problems with equality and box inequality constraints. The …
Sen Na
,
Yihang Gao
,
Michael K. Ng
,
Michael W. Mahoney
Cite
PDF
Convergence Analysis of Accelerated Stochastic Gradient Descent under the Growth Condition
We study the convergence of accelerated stochastic gradient descent for strongly convex objectives under the
growth condition
, which …
You-Lin Chen
,
Sen Na
,
Mladen Kolar
Cite
arXiv
Online Covariance Matrix Estimation in Stochastic Inexact Newton Methods
Online algorithms gain prominence as the volume of data explodes, among which second-order methods are known for their robustness. …
Wei Kuang
,
Sen Na
,
Michael W. Mahoney
,
Mihai Anitescu
Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching
We consider solving equality-constrained nonlinear, nonconvex optimization problems. This class of problems appears widely in a variety …
Ilgee Hong
,
Sen Na
,
Michael W. Mahoney
,
Mladen Kolar
Cite
arXiv
A Fast Temporal Decomposition Procedure for Long-horizon Nonlinear Dynamic Programming
We propose a
fast
temporal decomposition procedure for solving long-horizon nonlinear dynamic programs. The core of the procedure is …
Sen Na
,
Mihai Anitescu
,
Mladen Kolar
Cite
DOI
arXiv
Inequality Constrained Stochastic Nonlinear Optimization via Active-Set Sequential Quadratic Programming
We study nonlinear optimization problems with a stochastic objective and deterministic equality and inequality constraints, which …
Sen Na
,
Mihai Anitescu
,
Mladen Kolar
Cite
DOI
arXiv
Trust-Region Sequential Quadratic Programming for Stochastic Optimization with Random Models
We design a trust-region sequential quadratic programming (TR-SQP) method to find both
first- and second-order
stationary points for …
Yuchen Fang
,
Sen Na
,
Michael W. Mahoney
,
Mladen Kolar
A Theoretically Sound Sequential Quadratic Programming Algorithm On Riemannian Manifolds
We design a Sequential Quadratic Programming (SQP) algorithm for solving equality-constrained optimization problems on Riemannian …
Miao Li
,
Sen Na
,
Mladen Kolar
Hessian Averaging in Stochastic Newton Methods Achieves Superlinear Convergence
We consider minimizing a smooth and strongly convex objective function using a stochastic Newton method. At each iteration, the …
Sen Na
,
Michał Dereziński
,
Michael W. Mahoney
Cite
DOI
arXiv
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